Chapter 5, Excercise 10

Hello,

to complete this exercise I directly implemented LinearSVC into the coding of Chapter 2.
As data I used `housing_prepared` and as labels I used `housing_labels`.
In the end I just trained the SVM regressor like that:

```
svm_reg = LinearSVR(random_state=42)
svm_reg.fit(housing_prepared, housing_labels)
```

The score is quite unusual (compared to RandomForest and Linear Regression):

```
housing_predictions = svm_reg.predict(housing_prepared)
svm_reg_mse = mean_squared_error(housing_labels, housing_predictions)
svm_reg_rmse = np.sqrt(svm_reg_mse)
svm_reg_rmse

218339.15956036837
```
Why is it so badly underfitting the data? Why is the error in the exercise 10 so much lower, although the data should be more or less the same (Housing_prepared additionally is scaled, uses the imputer and the OneHotEncoder for Ocean Aprox. and adds some attributes).

thanks